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Absolutely you can do 'AI Research' without a degree in maths. Sure you need a grounding in linear algebra, stats, probability and calculus, but not much more t
by joshvm 9y ago
Absolutely you can do 'AI Research' without a degree in maths. Sure you need a grounding in linear algebra, stats, probability and calculus, but not much more than a CS or physics degree will teach you. That stuff is indeed learned easily, and it's not what the Reddit user is worried about.
That also ignores applications of machine learning, which is also a massive (and lucrative) field. But because it's a trendy field, I think there is an obsession with people needing to understand everything theoretical that comes out for fear of missing the boat.
Some of the really interesting papers that have come out over the last few years - for example artistic style transfer and Faster R-CNN - have hardly any maths in. You can count the equations on one hand in both those papers. No doubt the authors know their stuff, but how readable are those papers compared to e.g. a 100-page proof? Which did I learn more from?
They're a combination of two things: intuitive network architecture and a clever loss function. The first thing is a combination of intuition and programming, the second involves a little maths, but nothing outrageous.
[1] https://arxiv.org/abs/1508.06576 https://arxiv.org/abs/1508.06576
[2] https://arxiv.org/abs/1506.01497 https://arxiv.org/abs/1506.01497
- afpx 9y agoYou're right - I see now when he says 'AI Research' he really means doing applied ML. And, that can certainly be done without knowing everything. If the goal is to make a lot of money doing applied ML, then become a consultant and aim to know 10% more than the customers. If the goal is to create models that are relatively effective, then read tutorials, play with data, experiment, and iterate. But, if the goal is to create very effective models and be able to actually explain why they work (which I think is what many companies want), then one has to understand the math. That is, showing some interesting relationships, trends, predictions, or inferences on a data analysis portal or consumer web site is one thing. But, using ML to dispense medication, regulate a medical device, drive a power plant, or identify criminal suspects - those may require different skills. (BTW I don't mean to disparage the middle group, as that's largely what I do. But, luckily I have people in the latter group who can validate what I'm doing.)
- Blazespinnaker 9y agoNot a degree, no, but you should have depth in the math techniques you are using to solve your problem. You should also develop a high level abstracted understanding of math to have the intuition that your techniques are likely the right ones. This approach will help make incremental improvements. You might even get lucky and hit in something that cites really well.